Performix Site

List the performance-science reading corpus behind Performix — book/cluster profiles, each with a stance toward the CAMS diagnostic thesis (ally, orthodoxy, mixed, leave-out) and a one-line argument summary. Optional `query` filters by a case-insensitive substring match against title, authors, tradition and blurb.

Get the full grounded profile for one book/cluster record by slug (from list_books): the record's own argument (contrast-not-summary) plus metadata and cross-links. Same content rendered publicly at performix.app/learn/books/<slug>.

List the 196 source records behind Performix's Performance Science Library — each a book, paper or corpus item with at least one derived finding, plus which CAMS dimensions (capability/alignment/motivation/support) its findings touch. Same source list rendered at performix.app/learn/evidence. NOTE: 132 of 196 sources carry an upstream placeholder citation title pending backfill (HO-2431) — check `citationPlaceholder` before treating `title`/`venue` as a real bibliographic record. Optional `query` filters by a case-insensitive substring over authors/title/venue; optional `camsDimension` filters to sources with at least one finding filed on that dimension.

Get the full source → finding → construct → survey-item evidence chain for one source by id (from list_evidence_sources): its citation, every finding extracted from it, the constructs derived from each finding, and the survey items derived from each construct. Same content rendered at performix.app/learn/evidence/<id>. HONESTY: the citation is exposed as-is, including the 132 sources still carrying a placeholder title (HO-2431) — `citationPlaceholder` says which.

List the constructed outcome-model register behind Performix diagnostics — 28 per-outcome factor models generated from principia's meta-analytic library, each with its driver count, grade-A driver count, CAMS filing counts, and persona reach. Same register rendered at performix.app/learn/models. Also returns `namedUnmodellable` — outcomes named in the catalog with no constructed model yet. MEASUREMENT-INTEGRITY (PFX-589): this list carries no effect sizes at all; call get_outcome_model for a driver table, which withholds `r` below evidence tier T3 exactly as the public page does.

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Get one outcome's full driver table by id (from list_outcome_models): every driver's name, definition, CAMS dimension, lens role, evidence grade, study count (k), and evidence-reliability tier — plus drivers cited in the corpus with no meta-analytic effect size, and the personas that own this outcome. Same content rendered at performix.app/learn/models/<id>. MEASUREMENT-INTEGRITY (PFX-589, a portfolio standing rule): a driver's `r` is null unless its tier is T3 — this tool withholds exactly what the public page withholds and nothing less; VOI figures and overlap-corrected variance contributions are never exposed by this tool at any tier.

List all 12 parts (8 numbered parts + 4 appendices) of the Team Performance Science Guide, Performix's cited public reference on why team performance varies and the CAMS diagnostic frame — same table of contents rendered at performix.app/learn. Each row carries `status` (live | draft | stub) so a caller can see which parts are actually publishable before calling get_guide_part.

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Get the full body of one Team Performance Science Guide part by slug (from list_guide_parts). Same content rendered at performix.app/learn/<slug>. A part whose `status` is `stub` has no publishable body yet and returns an error rather than a placeholder — check `list_guide_parts`' `status` field first if you want to skip those.

The runnable library's weakest-lever read, computed here from the committed book model: rate how present each of the book's levers is on your team (1–5) and get the one to work on first, ranked by weakness then causal leverage — the SAME function performix.app/learn/library uses. Levers come from list_books' exec-shelf slugs (e.g. the-culture-code). N=1, self-report, not psychometrically calibrated: tier INFERRED. Refuses degenerate ratings (fewer than two levers rated, all ratings equal, or values outside 1–5) instead of inventing an answer.

Get a committed situation-demo export for an outcome and audience seat. The data is synthetic and illustrative, never a real client or live measurement. Visual series may be trimmed to stay within the response-size budget.